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| import gradio as gr | |
| import tensorflow as tf | |
| import dependency | |
| #hard to make it work as depend on environment | |
| # MODEL = "cn_to_en_transformer.keras" | |
| # transformer = tf.keras.models.load_model(MODEL) | |
| EMBEDDING_DEPTH = dependency.EMBEDDING_DEPTH | |
| MAX_TOKENIZE_LENGTH = dependency.MAX_TOKENIZE_LENGTH | |
| tokenizer_cn = dependency.tokenizer_cn | |
| tokenizer_en = dependency.tokenizer_en | |
| num_layers = 1 | |
| d_model = EMBEDDING_DEPTH | |
| dff = MAX_TOKENIZE_LENGTH | |
| num_heads = 8 | |
| dropout_rate = 0.1 | |
| # Create a new model instance | |
| transformer = dependency.Transformer(num_layers=num_layers, d_model=d_model, num_heads=num_heads, dff=dff,\ | |
| input_vocab_size=tokenizer_cn.vocab_size,target_vocab_size=tokenizer_en.vocab_size, dropout_rate=dropout_rate) | |
| transformer.load_weights('./checkpoints/cn_to_en_transformer_checkpoint') | |
| def preprocess(text): | |
| text = tf.constant(tokenizer_cn.encode(text, add_special_tokens=True))[tf.newaxis] | |
| return text | |
| def inference(text): | |
| start_end = tokenizer_en.encode("", add_special_tokens=True) | |
| start = tf.constant(start_end[0],dtype=tf.int64)[tf.newaxis] | |
| end = tf.constant(start_end[1],dtype=tf.int64)[tf.newaxis] | |
| # `tf.TensorArray` is required here (instead of a Python list), so that the | |
| # dynamic-loop can be traced by `tf.function`. | |
| output_array = tf.TensorArray(dtype=tf.int64, size=0, dynamic_size=True) | |
| output_array = output_array.write(0, start) | |
| for i in tf.range(MAX_TOKENIZE_LENGTH): | |
| output = tf.transpose(output_array.stack()) | |
| predictions = transformer([text, output], training=False) | |
| # Select the last token from the `seq_len` dimension. | |
| predictions = predictions[:, -1:, :] # Shape `(batch_size, 1, vocab_size)`. | |
| predicted_id = tf.argmax(predictions, axis=-1) | |
| # Concatenate the `predicted_id` to the output which is given to the | |
| # decoder as its input. | |
| output_array = output_array.write(i+1, predicted_id[0]) | |
| if predicted_id == end: | |
| break | |
| text = tf.transpose(output_array.stack()) | |
| return text | |
| def postprocess(text): | |
| text = tokenizer_en.decode(text[0], skip_special_tokens=True) | |
| return text | |
| def translate(text): | |
| if (text.strip()==""): | |
| return "" | |
| text = preprocess(text) | |
| text = inference(text) | |
| return postprocess(text) | |
| DESCRIPTION = "" | |
| DESCRIPTION += "<h1>中英翻译器</h1>" | |
| DESCRIPTION += "<h1>Chinese to English translator</h1>" | |
| DESCRIPTION += "<p>This translator is building by using transformer from scratch</p>" | |
| DESCRIPTION += "<p>This is just a demonstration of the usage of transformer, the translation is not 100% correct</p>" | |
| DESCRIPTION += "<ul><li><a href=\"https://medium.com/@jiachiewloh/nlp-chinese-to-english-translation-by-using-transformer-6503c1f4a139\">Article</li>" | |
| DESCRIPTION += "<li><a href=\"https://www.kaggle.com/code/jclohjc/cn-en-translation-using-transformer\">Code</li></ul>" | |
| with gr.Blocks(css="styles.css") as demo: | |
| gr.HTML(DESCRIPTION) | |
| #the input and output | |
| with gr.Row(): | |
| input_text = gr.Text(label="中文 (Chinese)",\ | |
| info="请输入您想翻译的句子(Please enter the text to be translated)") | |
| output_text = gr.Text(label="English (英文)",\ | |
| info="Here is the translated text (这是翻译后的句子)") | |
| with gr.Row(): | |
| gr.Button("Translate").click(fn=translate,inputs=input_text,outputs=output_text) | |
| gr.ClearButton().add([input_text,output_text]) | |
| #Examples | |
| gr.Examples(examples=[["祝您有个美好的一天","Have a nice day"], ["早上好，很高兴见到你","Good Morning, nice to meet you"], | |
| ["你叫什么名字","What is your name"],["我喜欢爬山","I like climbing"],["我爱你","I love you"], | |
| ["我是一个好人","I am a good person"],["我们是一家人","We are family"]],\ | |
| inputs=[input_text,output_text],\ | |
| outputs=[output_text]) | |
| demo.launch() | |